Two common issues in synchronized multimodal recordings with EEG: Jitter and latency

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Multimodal recordings combining electroencephalogram (EEG) and other biological signals are essential in human neuroscience.
• Precise time alignment of data from heterogeneous sources is challenging due to variable device parameters and complex setups.
• Jitter and latency are common issues in multimodal data acquisition.

Purpose of the Study:

• To introduce a Lab Streaming Layer (LSL)-based application for overcoming jitter and latency in multimodal recordings.
• To discuss the benefits of time-synchronization for multi-device recordings.
• To highlight the impact of jitter on electrophysiological outcomes via computer simulation.

Main Methods:

• Review of Lab Streaming Layer (LSL) applications for multimodal data acquisition.
• Discussion of jitter and latency issues in neuroscience research.
• Computer simulation to quantify the effect of jitter on signal-to-noise ratio.

Main Results:

• A Lab Streaming Layer (LSL)-based system effectively mitigates jitter and latency in multimodal recordings.
• Millisecond-order jitter significantly degrades the signal-to-noise ratio of electrophysiological data.
• Precise time-alignment enhances the reliability of multimodal neuroscience findings.

Conclusions:

• LSL-based systems are versatile tools for research requiring precise temporal alignment of multimodal datasets.
• Studies involving stimulus-induced neural responses or temporal relationships benefit significantly from LSL-based synchronization.
• Implementing LSL facilitates more accurate and robust multimodal neuroscience research.